A fuel cell is an electrochemical device that converts chemical energy, derived from a fuel, into electrical energy. The use of different types and designs of fuel cells is expected by many observers to increase dramatically in the future as fuel cells are employed as mobile and stationary power sources for powering an ever-expanding array of devices. Accordingly, there is heightened interest in techniques for accurately and efficiently modeling the operative behavior of different types and designs of fuel cells.
The operative responses of a fuel cell typically depend on a number of different factors, such at the fuel and oxidant feed, flow, and pressure, as well as temperature within the fuel cell. An important objective in modeling a fuel cell is to capture the transient behavior of the fuel cell associated with various factors such as flow and inertia dynamics and reactant partial pressures.
Presently, two general classes of dynamic fuel cell models are widely employed. One type of dynamic fuel cell model is the empirical-based model of a fuel cell. Empirical-based models are typically modified versions of a steady-state model in which a capacitive element is incorporated in order to describe transient response properties of the fuel cell being modeled. Such models are usually combined with so-called lumped electrical circuit elements whose internal physical properties are largely suppressed in favor of an overall terminal description.
Empirical-based models offer an advantage in so far as the parameters associated with the electrical circuit elements are usually easy to obtain from experimental data derived from the operation of the particular fuel cell being modeled. These models are limited, however, in that most can not be used to model important transient phenomena such as voltage overshooting and undershooting of the fuel cell.
The other general class of dynamic fuel cell models comprise mathematical-based models. These models typically comprise a number of time-based partial differential equations derived from the electrochemical, electronic, mechanical, thermal, and other theoretical properties of the fuel cell being modeled. The mathematical-based models are generally capable of modeling transient response properties stemming from the intrinsic physical characteristics of a fuel cell. Such models, however, are typically very complex, thus requiring considerable calculation to adequately model a particular fuel cell. Moreover, at least some of the parameters associated with the different equations of such a model can be very difficult to determine.
It follows that there is a need for a more effective and efficient technique for modeling a fuel cell and fuel stack systems. Specifically, there is a need for a dynamic fuel cell model that captures the transient behavior of the fuel cell while mitigating the computational demands imposed by elaborate and complex mathematical equations.